The Role of IgG Fc Region N-Glycosylation in the Pathomechanism of Rheumatoid Arthritis

Anti-citrullinated protein antibodies (ACPAs) are involved in the pathogenesis of rheumatoid arthritis. N-glycosylation pattern of ACPA-IgG and healthy IgG Fc differs. The aim of this study is to determine the relative sialylation and galactosylation level of ACPAs and control IgG to assess their capability of inducing TNFα production, and furthermore, to analyze the correlations between the composition of Fc glycans and inflammatory markers in RA. We isolated IgG from sera of healthy volunteers and RA patients, and purified ACPAs on a citrulline-peptide column. Immunocomplexes (IC) were formed by adding an F(ab)2 fragment of anti-human IgG. U937 cells were used to monitor the binding of IC to FcγR and to trigger TNFα release determined by ELISA. To analyze glycan profiles, control IgG and ACPA-IgG were digested with trypsin and the glycosylation patterns of glycopeptides were analyzed by determining site-specific N-glycosylation using nano-UHPLC-MS/MS. We found that both sialylation and galactosylation levels of ACPA-IgG negatively correlate with inflammation-related parameters such as CRP, ESR, and RF. Functional assays show that dimerized ACPA-IgG significantly enhances TNFα release in an FcγRI-dependent manner, whereas healthy IgG does not. TNFα production inversely correlates with the relative intensities of the G0 glycoform, which lacks galactose and terminal sialic acid moieties.

Introduction
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting the synovial membrane lining of the joints, eventually leading to irreversible joint damage and bone erosion. More than 70% of RA patients are seropositive to anti-citrullinated peptide antibodies (ACPAs) and other modified peptide antibodies (AMPAs) [1][2][3][4]. Citrullination is a posttranslational protein modification induced by the activated peptidyl arginine deiminase (PADI). ACPA is the most specific marker for RA; however, it not only has a Int. J. Mol. Sci. 2022, 23, 5828 2 of 16 diagnostic significance, but also, due to the pro-inflammatory properties of ACPA-IgG immune complexes, possibly contributes to the disease pathogenesis [5][6][7][8][9].
ACPA-IgG-containing immune complexes (ICs) bind to FcγRIIa on monocytes and macrophages, inducing tumor necrosis factor α (TNFα) release [5]. TNFα is the first inflammatory cytokine to appear, detected in the sera and synovial fluid of RA patients, and macrophages are the main source of TNFα in the synovium [5,10]. Three classes of FcγR are expressed on human monocytes and macrophages as well as U937 cells, a promonocytic, human myeloid leukemia cell line with monocyte morphology. FcγRI/CD64 is a high-affinity receptor, constitutively expressed at substantial levels. Monocytes express high levels of FcγRII/CD32, a low-affinity receptor for immunocomplexes (ICs) with two functionally distinct isoforms. FcγRIIa is an activatory receptor, triggering monocyte activation in response to receptor aggregation by immune complexes [11]. Only the activation of FcγRIIa was shown to induce TNFα production by monocytes [5]; however, the high affinity activatory FcγRI receptor is responsible for TNFα production in neutrophils [12]. FcγRIII/CD16, a receptor with moderate affinity for complexed IgG, is present only in a low level on circulating monocytes [7].
The mechanism behind the inflammatory property of ACPA is incompletely understood. Glycosylation of IgG Fc is considered a critical regulator of the antibody effector functions. Mature Fc glycoforms are complex biantennary sugar moieties at asparagine 297 of the IgG CH2 domains [13]. The core Fc glycan is composed of four N-acetylglucosamine (GlcNAc) and three mannose residues [14]. The core glycan can be modified by additional sugars, including a core fucose, bisecting GlcNAc, as well as galactose and sialic acid at one or both arms [15].
Aberrant glycosylation of IgG in inflammatory autoimmune diseases has been observed [16][17][18], as well as a significantly lower level of N-galactosylation and sialylation, whereas higher fucosylation was detected on IgG from RA patients compared to healthy controls [19,20]. Sialylation has shown an inverse correlation with disease activity. According to a recent model, glycosylation of IgG is immunologically regulated, and the lower sialylation of IgG in RA is a result of the enhanced number/function of follicular T (Tfh) cells, and, in particular, Tfh17 cells downregulating sialyltransferase β-galactoside α-2,6-sialyltransferase 1 (ST6Gal I) in autoantibody-producing B cells. Consequently, the generated IgG is deficient in terminal sialic acid residues [21,22]. There is a close correlation between systemic inflammation and ACPA-IgG1 Fc sialylation in RA and other autoimmune diseases [18]. Low levels of total serum IgG or autoantibody sialylation have been observed in patients with a variety of autoimmune diseases such as RA, antiphospholipid syndrome (APS), vasculitis, or SLE [20,[23][24][25][26]. Some studies suggest that the two CH2 domains of highly sialylated IgG might be more likely to form a closed conformation, impairing the binding to FcγR [27][28][29]. However, others reveal no or a slight effect of sialylation on IgG Fc structure and an unaltered binding affinity to activating FcγR [27,30,31].
To investigate the functional differences between immune complexes formed by ACPA-IgG and control IgG from healthy volunteers, we applied U937 cells, a pro-monocytic, human myeloid leukemia cell line with monocyte morphology, as a model to study the proinflammatory activities of small (dimeric) IC. ACPA-IgG complexes induced significantly higher TNFα release compared to control IgG and was dependent on the binding of IC to FcγRI. Determining the site-specific N-glycosylation profile of the IgG Fc region in the same cohort, we observed an inverse correlation between TNFα production and the relative intensity of N4H3 glycan corresponding to the G0 glycoform. Analyzing the correlations with inflammation-related markers, we found a negative correlation between RF, CRP, and ESR values and the level of both galactosylation and sialylation of ACPA-IgG. We also confirmed earlier results showing that both the sialylation and galactosylation level of ACPA-IgG subclasses is significantly lower compared to IgG from healthy controls. Table 1. Citrulline peptide specificity, demographic, and clinical data of the investigated RA patients. Citrulline peptide reactivity is expressed as the OD index, which is the optical density of the sample on citrulline-containing peptide divided by the optical density of the corresponding arginine-containing peptide. IgG from healthy controls, ACPA-IgG, and flow-through fractions from the citrulline peptide-coated columns were digested with trypsin and the glycosylation pattern of glycopeptide fragments were analyzed by determining site-specific N-glycosylation using nano-UHPLC-MS/MS. First, we analyzed the correlations between inflammation-related parameters of RA patients and the levels of the main glycoforms. Based on the analysis of 77 glycopeptides from IgG Fc N-glycan, the galactosylation level of ACPA-IgG highly negatively correlated with RF titers and showed a moderately negative correlation with CRP and ESR. The sialylation level showed a moderately negative correlation with all three markers, whereas the level of fucosylation, which mainly affects the binding of IgG to FcγRIII [33,34] showed no correlation. Interestingly, the ACPA-IC-induced in vitro TNFα production did not correlate with the clinical inflammatory markers, whereas the level of bisecting GlcNAc moderately correlated with RF and ESR. We also observed a highly positive correlation between RF, ESR, and CRP ( Figure 1).  [33,34] showed no correlation. Interestingly, the ACPA-IC-induced in vitro TNFα production did not correlate with the clinical inflammatory markers, whereas the level of bisecting GlcNAc moderately correlated with RF and ESR. We also observed a highly positive correlation between RF, ESR, and CRP ( Figure 1). We compared the relative intensity values of galactosylation and sialylation of ACPA-IgG1, IgG2, and IgG3 subclasses from RA patients and IgG1, IgG2, and IgG3 from healthy controls. N-glycopeptides from the different isotypes of IgG were differentiated based on their amino acid sequences. The discrimination of intact IgG3 and IgG4 N-glycopeptides with high similarity in sequence is not possible with the method used [35]; thus, these were measured together but are labeled "IgG3" in the text.

Demographic and Clinical Parameters of Patients
Both galactosylation and sialylation levels of all IgG subclasses were significantly lower in ACPA compared to healthy samples, confirming earlier results where the analysis of glycans was carried out with a different method: linear ion-trap electrospray ionization mass spectrometry (LTQ-ESI-MS) ( Figure 2). The highest differences were detected in the IgG1 samples.
To exclude individual differences from the analysis, we also compared ACPA-IgG and non-ACPA IgG (the flowthrough fractions from the citrulline peptide columns) from the same patient. The level of galactosylation and sialylation was significantly lower in ACPA compared to non-ACPA fractions, with the latter falling in the range of healthy control IgG (Figure 3). We compared the relative intensity values of galactosylation and sialylation of ACPA-IgG1, IgG2, and IgG3 subclasses from RA patients and IgG1, IgG2, and IgG3 from healthy controls. N-glycopeptides from the different isotypes of IgG were differentiated based on their amino acid sequences. The discrimination of intact IgG3 and IgG4 N-glycopeptides with high similarity in sequence is not possible with the method used [35]; thus, these were measured together but are labeled "IgG3" in the text.
Both galactosylation and sialylation levels of all IgG subclasses were significantly lower in ACPA compared to healthy samples, confirming earlier results where the analysis of glycans was carried out with a different method: linear ion-trap electrospray ionization mass spectrometry (LTQ-ESI-MS) ( Figure 2). The highest differences were detected in the IgG1 samples. To exclude individual differences from the analysis, we also compared ACPA-IgG and non-ACPA IgG (the flowthrough fractions from the citrulline peptide columns) from the same patient. The level of galactosylation and sialylation was significantly lower in ACPA compared to non-ACPA fractions, with the latter falling in the range of healthy control IgG ( Figure 3).

Dimeric Immune Complexes of ACPA-IgG and Control IgG Bind to FcγRI o Cells
We chose U937 cells as a model cell line to study the binding and functiona of various IgG immune complexes. The immune complexes were formed by F(ab)'2 fragments of anti-human IgG Fab labeled with Alexa 647 to purified A control IgG (10 µ g/mL). The stoichiometry of the components allows the form dimeric IgG complexes, which has in vivo relevance [12]. The expression of PMA-stimulated U937 cells was checked by monitoring the binding of specific an against FcγRI, FcγRII, and FcγRIII by flow cytometry. As expected, FcγRI an were readily detected but FcγRIII was only negligibly expressed on the cell line 4a). Next, we compared the binding capacity of dimeric immune complexes fo ACPA-IgG and healthy IgG and found that both complexes bound to the U937 ACPA complexes exhibited a decreased mean fluorescent intensity compared to IgG (Figure 4b). To test whether type I or type II FcγR were responsible for the we applied blocking antibodies. After pre-treating the cells with FcγRI-or FcγRI blocking antibodies, we observed that only anti-FcγRI could inhibit the bindi cating that the dimeric immune complexes can occupy primarily FcγRI on the ce (Figure 4c).

Dimeric Immune Complexes of ACPA-IgG and Control IgG Bind to FcγRI on U937 Cells
We chose U937 cells as a model cell line to study the binding and functional activity of various IgG immune complexes. The immune complexes were formed by adding F(ab)' 2 fragments of anti-human IgG Fab labeled with Alexa 647 to purified ACPA or control IgG (10 µg/mL). The stoichiometry of the components allows the formation of dimeric IgG complexes, which has in vivo relevance [12]. The expression of FcγR on PMA-stimulated U937 cells was checked by monitoring the binding of specific antibodies against FcγRI, FcγRII, and FcγRIII by flow cytometry. As expected, FcγRI and FcγRII were readily detected but FcγRIII was only negligibly expressed on the cell line ( Figure 4a). Next, we compared the binding capacity of dimeric immune complexes formed by ACPA-IgG and healthy IgG and found that both complexes bound to the U937 cells, but ACPA complexes exhibited a decreased mean fluorescent intensity compared to healthy IgG ( Figure 4b). To test whether type I or type II FcγR were responsible for the binding, we applied blocking antibodies. After pre-treating the cells with FcγRI-or FcγRII-specific blocking antibodies, we observed that only anti-FcγRI could inhibit the binding, indicating that the dimeric immune complexes can occupy primarily FcγRI on the cell surface ( Figure 4c).

Dimeric ACPA Immune Complexes Induce Significantly Higher TNFα Release from U937 Cells Compared to Complexes Containing IgG from Healthy Individuals
Pro-inflammatory activity of immune complexes is mediated through their binding to the activating FcγR and the induction of TNFα production. Therefore, we compared dimeric immune complexes by measuring TNFα from the supernatants of U937 cells pre-treated with IC-containing ACPA IgG or control IgG complexes for 24 h. Pre-formed complexes were prepared for 30 min at 37 °C and then added to the cells. ACPA-IgG complexes triggered a significantly higher TNFα secretion compared to healthy IgG, indicating that despite the lower binding to the cells, ACPA-IgG has higher pro-inflammatory activities (Figure 5a). On the other hand, when only ACPA IgG was added to the cells without the crosslinking with the F(ab)2 fragment of anti-IgG Fab antibodies, we could not observe any difference in TNFα production compared to the untreated control cells (Figure 5b). To assess FcγR dependency of TNFα production, we pre-treated the cells with FcγRI-blocking antibody, which, similar to the IC binding, significantly reduced IC-induced TNFα release from U937 cells (Figure 5c).

Dimeric ACPA Immune Complexes Induce Significantly Higher TNFα Release from U937 Cells Compared to Complexes Containing IgG from Healthy Individuals
Pro-inflammatory activity of immune complexes is mediated through their binding to the activating FcγR and the induction of TNFα production. Therefore, we compared dimeric immune complexes by measuring TNFα from the supernatants of U937 cells pretreated with IC-containing ACPA IgG or control IgG complexes for 24 h. Pre-formed complexes were prepared for 30 min at 37 • C and then added to the cells. ACPA-IgG complexes triggered a significantly higher TNFα secretion compared to healthy IgG, indicating that despite the lower binding to the cells, ACPA-IgG has higher pro-inflammatory activities (Figure 5a). On the other hand, when only ACPA IgG was added to the cells without the crosslinking with the F(ab)2 fragment of anti-IgG Fab antibodies, we could not observe any difference in TNFα production compared to the untreated control cells (Figure 5b). To assess FcγR dependency of TNFα production, we pre-treated the cells with FcγRI-blocking antibody, which, similar to the IC binding, significantly reduced IC-induced TNFα release from U937 cells (Figure 5c).
Brefeldin A for 5 h and TNFα was detected in the fixed and permeabilized cells by i tracellular staining. We observed a significantly higher intensity of staining with t PE-conjugated anti-TNFα antibody in ACPA-IC-treated compared to control IgG-treat U937 cells, indicating that ACPA IC binding to FcγRI indeed induces TNFα synthes (Figure 5d).

TNFα Production Induced by ACPA-Containing Immune Complexes Shows a Negative Correlation with the Relative Percentage of N4H3 Glycan of IgG Fc
TNFα plays important functions in the pathogenesis of RA; TNFα antagonists a widely applied in therapy and have shown remarkable efficacy in various im mune-mediated inflammatory diseases, including RA. ACPA-containing IC was show to induce TNF-α production by macrophages [5]. Our aim here was to reveal whether t TNFα-inducing capacity of ACPA IC associates with the altered glycoforms of A Finally, to control whether ACPA IC indeed triggers TNFα synthesis, the IC-treated cells were additionally stimulated with PMA and ionomycin in the presence of Golgi stop, Brefeldin A for 5 h and TNFα was detected in the fixed and permeabilized cells by intracellular staining. We observed a significantly higher intensity of staining with the PE-conjugated anti-TNFα antibody in ACPA-IC-treated compared to control IgG-treated U937 cells, indicating that ACPA IC binding to FcγRI indeed induces TNFα synthesis (Figure 5d).

TNFα Production Induced by ACPA-Containing Immune Complexes Shows a Negative Correlation with the Relative Percentage of N4H3 Glycan of IgG Fc
TNFα plays important functions in the pathogenesis of RA; TNFα antagonists are widely applied in therapy and have shown remarkable efficacy in various immunemediated inflammatory diseases, including RA. ACPA-containing IC was shown to induce TNF-α production by macrophages [5]. Our aim here was to reveal whether the TNFαinducing capacity of ACPA IC associates with the altered glycoforms of ACPA-IgG Fc. We identified a total of 77 glycopeptides in ACPA IgG Fc; of these only the N4H3 representing the agalactosylated IgG with the biantennary oligosaccharide terminating in GlcNAc showed a high negative correlation with the TNFα-inducing capacity of ACPA-IC ( Figure 6).

Discussion
ACPA might contribute to RA pathogenesis through a variety of mechanisms. It binds directly to citrullinated self-antigens on the surface of cells or interact with different Fc receptors, thereby modulating the function of many cell types [9]. N-glycosylation of IgG Fc at Asn 297 highly influences the effector functions. The glycosylation pattern of ACPA-IgG differs from that of healthy controls; the lower level of ACPA-IgG sialylation and galactosylation is associated with higher inflammatory properties [23,25,36]. Sialic acid binds to the biantennary structure of the glycan terminating in one or two galactoses; thus, agalactosylated IgG Fc (IgG G0 form) has no sialic acid and has high proinflammatory activity [15,16]. Autoantibodies with low sialic acid and/or no galactose form circulating immune complexes that can activate inflammatory cells, eventually resulting in the development of autoimmune diseases. However, the underlying mechanism of how hyposialylated and hypogalactosylated IgG might induce inflammation is not purely understood. The inflammatory potential of ACPA IC was first demonstrated by Clavel et al., show demonstrated that the insolubilized immune complexes induce TNFα release via engagement of the activating FcγRIIa on macrophages [5]. Other studies have shown that soluble dimerized IgG ACPA complexes preferentially bind to FcγRI on activated neutrophils, stimulating potential effector mechanisms by which ACPA could contribute to RA pathogenesis [12]. Additionally, it was found that ACPA and RF working in conjunction elicit a synergistic effect via IC formation enhancing inflammation [10,37].
The aim of this study was to reveal whether the structure of N-glycan on ACPA-IgG Fc correlates with its pro-inflammatory capacity in vivo and in vitro. To study the in vitro effect, we used the PMA-activated U937 cell line, which expresses FcγRI and FcγRII and practically no FcγRIII, and monitored TNFα release induced by ACPA-containing and IgGcontaining IC. In vivo inflammation was followed by monitoring inflammation-associated factors such as RF, CRP, and ESR in the same cohort of patients. The N-glycan profile of ACPA-IgG and healthy IgG was assessed by comparing the relative percentages of galactose, sialic acid, and fucose in the glycopeptides we identified.
Comparing the level of galactosylation and sialylation with the clinical inflammatory markers, we found that both showed an inverse correlation with RF, CRP, and ESR ( Figure 1). This is in concert with the previously described high pro-inflammatory capacity of hyposialylated, hypogalactosylated ACPA IgG [15,16,19,23].
The level of fucosylation did not correlate with clinical inflammatory markers, and a low positive correlation was found with the level of bisecting GlcNac. IgG1 Fc glycans lacking a core fucose residue (afucosylated Fc glycans) have higher affinity for FcγRIIIa (and FcγRIIIb) [33]. FcγRIIIa mediates antibody-mediated cellular cytotoxicity (ADCC) and plays a role in virus infections [34], but apparently does not influence inflammation. The effects of bisecting GlcNAc on IgG function are not well understood. Some studies indicate a role for bisection in the modulation of FcγRIIIa-mediated activities; however, data on this are not consistent [38,39].
Interestingly, the in vitro TNFα-inducing capacity of ACPA-IgG complexes did not correlate with the clinical inflammatory markers of patients, indicating that although TNFα plays important functions in the pathogenesis of RA, it is only one element of a complex network regulating inflammatory processes. Although the systemic effect of ACPA-ICinduced TNFα production cannot be seen, it still might have a local effect on the inflamed synovium. Furthermore, TNFα can also be produced by T cells independently of FcγRI, which might make a major contribution to the development of chronic inflammation in RA patients.
Analyzing the glycosylation level of ACPA-IgG, in agreement with previous results [20], we found that all subclasses of ACPA IgG had a significantly lower level of galactosylation and sialylation compared to control IgG subclasses ( Figure 2). Thus, aberrant glycosylation may influence the effector functions mediated by all IgG subclasses.
We compared galactosylation and sialylation levels of affinity-purified ACPA-IgG and the flow-through fractions from the peptide column used for the isolation containing the non-ACPA IgG of the same patient. Both galactosylation and sialylation levels of non-ACPA-IgG was significantly higher compared to ACPA-IgG, indicating that the IgG glycosylation level is reduced only in the pathogenic antibody-secreting cells (Figure 3). This is in concert with a recent model suggesting that the lower sialylation of IgG in RA is a consequence of the enhanced number/function of follicular T (Tfh) cells that negatively regulate sialyltransferase β-galactoside α-2,6-sialyltransferase 1 (ST6Gal I) in autoantibodyproducing B cells. [21,22].
Immune complex-binding assays showed that ACPA-IgG dimers bound to FcγRI on U937 cells with lower intensity, providing a lower MFI compared to control IgG (Figure 4b). Sialylation of IgG may modulate its function by binding to sialic acid-binding immunoglobulin-like lectins (Siglecs), such as Siglec-9 and Siglec-3 (CD33) [40]. Siglec-9 is expressed at a very low level, but CD33 is highly expressed on U937 cells and macrophages [41]. Thus, sialylated IgG from healthy volunteers might have simultaneously occupied both FcγRI and CD33, resulting in a higher binding avidity to the cells. On the other hand, Siglec-3 is an immune-modulatory receptor with an immunomodulatory tyrosine-based inhibitory motif (ITIM) within its intracellular domain. After being phosphorylated in FcγR-activated cells, ITIM recruits SHP-1 and SHP-2 phosphatase downregulating activation signals. Such a mechanism might contribute to the anti-inflammatory effect of highly sialylated IgG.
In earlier studies it was shown that ACPA IC stimulated TNFα release by binding to FcγRIIa, the activating receptor on macrophages [5]. In this study we found that despite the lower binding to U937 cells, most importantly, ACPA-IgG dimers but not monomeric ACPA triggered a significantly higher TNFα secretion compared to control IgG dimers (Figure 5a,b). We detected TNFα both in the supernatants of cells and as an intracellularly synthesized protein. Blocking antibodies to FcγRII did not affect either the binding of ACPA dimers to U937 cells or the enhanced TNFα production, whereas FcγRI-blocking antibodies partially prevented both effects (Figure 5c). We suppose that the different composition and size of immune complexes might explain the difference between earlier findings and the present results. Insolubilized large immune complexes can bind to the lower-affinity FcγRIIa, but in our experiments, however, dimerized IgG preferentially interacted with the high-affinity FcγRI and induced TNFα secretion ( Figure 5). Although in vivo FcγRI is probably occupied by serum IgG, immune complexes might compete with IgG binding to the de novo expressed FcγRI on activated cells, as suggested previously regarding activated neutrophils [12]; thus, the supposed mechanism might have in vivo relevance.
Fc glycan modifications directly affect the ability of IgG to interact with effector cells and dysregulation of the modifications can lead to loss of immune tolerance and autoimmunity, and infectious diseases [36]. Galactosylation of the Fc glycan is mediated by β-1,4-galactosyltransferase 1 (B4GALT1) and terminal sialylation is catalyzed by α-2,6sialyltransferase 1 (ST6GAL1), which adds sialic acid to the α-1,3 arm of the biantennary glycan [36], giving rise to the G1, G2, and G2S glycoforms with increasing anti-inflammatory activity [15]. Downregulation of ST6GAL1 in inflammatory autoimmune diseases may lead to the production of hyposialylated IgG by autoantibody-producing cells [42]. A direct role for galactosylated Fc glycans in the modulation of immune functions has not been defined. However, it was shown that IgG galactosylation associates with disease activity in pregnant women with RA [43]. Furthermore, galactosylation, independent of sialylation, is associated with improvement of RA during pregnancy [44].
We examined whether the capacity of ACPA-IgG complexes eliciting TNFα secretion depends on the IgG Fc glycan. Remarkably, we could not detect an association of TNFα production with the level of sialylation; however, in the set of 77 glycovariants analyzed, we identified one glycopeptide with the structure of N4H3, which showed a highly significant negative correlation with the TNFα-inducing capacity. This glycan did not contain galactose or sialic acid, derived from the G0 form of IgG that possesses the highest proinflammatory activity. A high level of the G0 form of IgG has been observed in several autoimmune diseases [25]. Thus, association of IgG G0 with the FcγRI-dependent TNFα production might contribute to the pathogenic effect of the autoantibodies in inflammatory autoimmune diseases in a sialylation-independent way.

Conclusions
We found that both sialylation and galactosylation levels of ACPA-IgG negatively correlate with inflammation-related clinical parameters in RA. Functional assays showed that dimerized ACPA-IgG significantly enhances TNFα release in a FcγRI-dependent manner, whereas healthy IgG does not. Finally, ACPA-IgG-induced TNFα production inversely correlates with the relative intensities of the G0 form of the IgG Fc glycan. We suggest that the association of agalactosylated IgG G0 with the FcγRI-dependent TNFα production might contribute to the pathomechanism of inflammatory autoimmune disorders such as RA.

Blood Samples
Blood samples were collected from RA patients diagnosed according to the revised classification criteria of the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) [45]. Blood samples were taken after the patients signed a written consent. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Ministry of Human Resources, Deputy State Secretary for National Chief Medical Officer, Department of Health Administration (21390-6-2017/EÜIG). Anti-CCP antibodies were obtained from positive female patients (n = 17, average age = 58 years). Age-and sex-matched control sera were obtained from healthy volunteers at the university (n = 20, average age = 52 years) who had not been vaccinated in the last three months and had no inflammatory or autoimmune diseases. Blood samples were collected in Vacuette 9 mL Z Serum clot activator tubes (Greiner Bio-one).

Serum Preparation
Fresh samples of blood were incubated at room temperature for an hour and then centrifuged at 800× g for 10 min. Then, the red blood cell-free serum was aspirated from the supernatant and stored in protein LoBind (Eppendorf) tubes at −20 • C until further use.

Determination of Anti-Citrullinated Protein Antibodies (ACPA) in Sera by ELISA
The ACPA content of the acquired serum samples was detected with an indirect ELISA as previously described [32,46,47]. Shortly, ELISA plates were pre-coated with 5 µg/mL neutravidine overnight at 4 • C to enhance peptide binding, and then were coated with selected citrulline or arginine-containing biotinylated peptides (see Table 2 for amino acid sequences) at 1 µg/mL concentration for an hour at 37 • C. Blocking was carried out with 2% BSA, 150 mM NaCl in PBS for 30 min at 37 • C. After rinsing, sera samples were added in a 1:100 dilution, and the plates were incubated overnight at 4 • C. After several washing steps, rabbit anti-human IgG(H+L)-HRPO detection antibody (Southern Biotech) was added in a 1:15,000 dilution and incubated at 37 • C for an hour. Development was carried out using TMB (Sigma) and the reaction was stopped using 2N H 2 SO 4 . The results were evaluated via photometry and OD indexes (OD on citrulline-containing peptide/OD on arginine-containing peptide) were determined. When the OD index >1.5 the sample was considered positive for ACPA and recommended for affinity purification on the corresponding citrulline peptide column. Multi-Epitope H-Ttds **-AXAXGSGSGXGXG-NH2 * In ELISAs the native forms of the peptides-containing arginine was also used. ** Ttds: Ttds, linker 1,13-diamino-4,7,10-trioxatridecan-succinamic acid.

Isolation of IgG
IgG was isolated from serum samples on Protein G columns (Sigma) according to the manufacturer's instructions. One mL of sample was measured onto the column with 1 mL PBS and left flowing through until the first visually detectable yellow drop appeared at the output valve. At this point the column was closed and incubated with the sample for 30 min at room temperature. Columns were then washed with 10 mL PBS and then IgG was eluted with 100 mM glycine, pH 2.5, into 1.5 mL tubes containing neutralization buffer. The eluted fractions were tested for protein content (Nanodrop ND-1000, Absorbance at 280 nm). Protein concentration was adjusted to 1 mg/mL and then dialyzed against PBS. The dialyzed samples were collected and stored at −20 • C until further use.

ACPA Isolation
ACPA were affinity purified from IgG fractions of RA patients on citrulline-containing peptides (provided by Anna Magyar and Katalin Uray at ELTE Peptide Chemistry Research Group) immobilized on NHS (n-hydroxi-succinimide) columns (Hi Trap ® , GE Healthcare, Uppsala, Sweden) as previously described [32]. The concentration of the eluted fractions was determined and adjusted to the 0.5-1 mg/mL range. The samples were then dialyzed against 50 mM NH 4 HCO 3 buffer 2 h at room temperature and then with a fresh batch of buffer overnight at 4 • C. Dialyzed fractions were checked again for IgG concentration and specificity, and then stored at −80 • C until further use.

Preparation of Immune Complexes
Artificial immune complexes (IC) were generated from 10 µg/mL ACPA-IgG or IgG from healthy controls by crosslinking two IgG molecules to form a dimer by adding Alexa Fluor 647-conjugated F(ab 2 )' fragment of anti-human IgG Fab (16 µg/mL) (Jackson ImmunoResearch) at the adequate stoichiometry [12] and incubated at 37 • C for an hour.

Immune Complex Binding Assay
U937 cells (ATCC, CRL-1593.2™) were used to study the binding and pro-inflammatory function of immune complexes. The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. Prior to adding IC, U937 cells were stimulated with 50 ng/mL PMA for 24 h. After stimulation, the cells were washed in sterile PBS and the IC were added to the cells and incubated for 30 min on ice in the dark, then washed with 1% FCS-PBS. The binding was measured via flow cytometry on a Beckman Coulter Cytoflex S flow cytometry system. The fluorescent signal was detected in the APC channel and the geometric means were calculated for each artificial immune complex. Giving the crosslinking F(ab2)' alone served as a negative control, since it is not to adhere to U937 cells.

Intracellular Staining to Monitor Inflammatory Cytokine TNFα Production
TNFα production was measured 24 h after IC addition to PMA-stimulated U97 cells. Five h prior to preparation a mixture of PMA (50 ng/mL), ionomycin (1 ug/mL), and brefeldin-a (×1000 dilution according to manufacturer protocol), also known as a PIB cocktail, was added to the cell cultures. Then, the cells were centrifuged at 300 rcf for 5 min at 4 • C, and supernatant was collected and stored at −20 • C until further use. BD Fix-Perm was added, and the cells were then incubated for 20 min on ice. An equal amount of BD Perm-Wash was added, and the cells were then centrifuged at 300 rcf for 5 min at 4 • C and the supernatant was discarded. The washing step was repeated with double the amount of BD Perm-Wash. Then, anti-TNFα antibody PE conjugate was added to the cells in BD Perm-Wash and incubated on ice for 30 min, washed out three times with BD Perm-Wash, and measured via flow cytometry. In all cases, the geometric mean of the fluorescent signal was calculated and compared.

TNFα Detection in the Supernatant of IC-Treated U937 Cells
As for the TNFα release assay, the previously PMA-stimulated U937 cells were incubated with the preformed IC made of 10 µg/mL IgG or ACPA and 16 µg/mL crosslinking F(ab2)' at 37 • C in a 5% CO2 atmosphere for 24 h, and then the cells were centrifuged at 300 rcf for 10 min and the supernatants were collected and stored at −20 • C until further use. The supernatants were tested for inflammatory cytokine release with a BioLegend ELISA Max™ anti-human TNFα ELISA kit according to the manufacturer's protocol.

Glycosylation Analysis
Samples collected from ACPA and total serum IgG isolation procedures were reduced, alkylated, and digested in solution with trypsin and Trypsin/Lys-C Mix at 37 • C as previously described [48]. A nano LC-MS(MS) system was used for peptide separation and glycopeptide analysis. The chromatographic separation was carried out on an Ultimate 3000 nanoRSLC system (Dionex, Sunnyvale, CA, USA). Samples were desalted using an Acclaim PepMap100 C-18 trap column (Thermo Scientific, Sunnyvale, CA, USA) and peptide separation was achieved with an Acquity UPLC M-Class Peptide BEH C18 column (Waters, Milford, MA, USA). A Maxis II ETD Q-TOF (Bruker Daltonics, Bremen, Germany) equipped with a CaptiveSpray nanoBooster ion source was used for the mass spectrometry measurements. Glycopeptides were identified by scanning the MS spectra over the mass range of m/z 150−3000 at 2 Hz. CID analysis was performed on triply charged precursor ions at 0.5 Hz for low-abundance ones (>2500 cts/s) and 4 Hz for abundant precursors (>25,000 cts/s). Glycopeptide quantification was based on MS experiments performed over the mass range of m/z 300−3000 at 1 Hz.

Further Statistical Analysis
Statistical analysis was carried out with the use of GraphPad Prism 5 and MS Excel software. Data from FcγR-blocking experiments and ACPA/flow-through experiments were tested with a two-tailed paired t test. Data from MS, IC binding, and TNFα release experiments were tested with a two-tailed unpaired t test to compare the difference between RA and healthy control groups.


Modelling intramuscular drug fate in vitro with tissue-relevant biomimetic hydrogels

Parenteral administrations are a mainstay of clinical drug delivery. Intramuscular (IM) injections deposit drug directly into skeletal muscle bellies, providing rapid systemic uptake due to the highly vascularized nature of this site. The potential to inject particulate or non-aqueous materials have also made IM injections useful for long-acting formulations. These attributes have supported a plethora of medicines being approved for IM administration. Despite these many approvals across multiple pharmaceutical categories, mechanisms that control drug release from the injection site, and thus its pharmacokinetic properties, remain poorly understood. Several pre-clinical in vivo animals have been used to model IM drug fate in patients, but these approaches have not consistently predicted clinical outcomes. This lack of a predictive in vivo model and no standardized in vitro tools have limited the options of pharmaceutical scientists to rationally design formulations for IM delivery. Here, we describe a novel, tractable in vitro model informed by dominant extracellular matrix (ECM) components present at the IM injection site. Three charge variants of green florescent protein (GFP) and the impact of three common formulation components were examined in an initial test of this in vitro model. A strongly positively charged GFP was restricted in its release from hydrogels composed of ECM components type I collagen and hyaluronic acid compared to standard and strongly negatively charged GFP. Introduction of commonly used buffers (histidine or acetate) or the non-ionic surfactant polysorbate 20 altered the release properties of these GFP variants in a manner that was dependent upon ECM element composition. In sum, this Simulator of IntraMuscular Injections, termed SIMI, demonstrated distinct release profiles of a protein biopharmaceutical surrogate that could be exploited to interrogate the impact of formulation components to expedite novel drug development and reduce current dependence on potentially non-predictive pre-clinical in vivo models.

Introduction
The intramuscular (IM) administration route is a common parenteral alternative to the oral delivery of pharmaceuticals that show poor physiochemical stability in, or limited uptake from, the gastrointestinal tract. The IM injection deposits formulations directly into skeletal muscle bellies, where diffusion through fascial planes of the skeletal muscle organ precedes uptake into the systemic vasculature or lymphatics (Newton et al., 1992;Ogston-Tuck, 2014). In general, drugs delivered by IM injection are formulated for immediate absorption using rapidly diffusing aqueous formulations that takes advantage of the highly vascularized nature of this tissue (e.g. epinephrine) or as longacting injectables (LAIs) which gradually release drug in a controlled manner over weeks or months (e.g. haloperidol decanoate) (Gu et al., 1999;Whyte and Parker, 2016). Despite the large number of drugs approved by regulators for IM injection, there is limited knowledge of how these drugs interact with the IM environment, i.e., drug fate (McCartan et al., 2021). At present there are no standardized in vitro tools available to researchers to investigate IM drug fate for the generation of descriptive pharmacokinetic (PK) data; the development of IMinjected drugs relies heavily on pre-clinical in vivo models, but these in vivo models fail to consistently predict human clinical outcomes (McCartan et al., 2021). Acellular in vitro modelling offers the potential for powerful, in-depth investigations that can address specific drug fate hypotheses in tractable experimental formats (McCartan et al., 2021).
established by the endomysium (space between individual muscle fibres) and perimysium (encapsulating individual muscle fibres into fascicles) within a skeletal muscle belly (Darville et al., 2016;Evertz et al., 2016;Groseclose and Castellino, 2019;Kalicharan et al., 2016;Kameni Tcheudji et al., 2016;Probst et al., 2016). The endomysium and perimysium compartments contain lymphatic and vascular capillaries, nerve fibres, and a variety of cells including adipocytes, fibroblasts, and macrophages, but is dominated by an extracellular matrix (ECM) composed of large macromolecules that organize and stabilize skeletal muscle fibres. A predominantly serum-derived interstitial fluid (ISF) is assumed to flow through the endomysium and perimysium. As very little of an IM-injected drug formulation enters individual muscle fibres, events of interaction with and dissemination through ECM elements of the endomysium and perimysium would dominate their fate following administration. Whilst the exact components and concentrations of ISF are still debated, ECM elements of the endomysium and perimysium are well documented with fibrillar collagen type 1 (Col1) being the dominant protein component (Gordon and Hahn, 2010;Loukas et al., 2008;Purslow, 2002) and hyaluronic acid (HA) being the predominant nonprotein element (Fraser et al., 1997;Piehl-Aulin et al., 1991). Previous efforts to model the IM injection environment have employed hydrogels composed of such ECM elements, including thiolmodified HA and glycosaminoglycans, and even decellularised/reconstituted animal skeletal muscle ECM (Narayanan et al., 2021;Prestwich, 2008;Wassenaar et al., 2015). Indeed, "micro-tissues" and whole excised muscle tissue have previously been described as in vitro and ex vivo IM-injection models, respectively (Afshar et al., 2020;Napaporn et al., 2000). Such models, however, are technically and financially challenging to use in large scale assays over long time scales. As such, acellular in vitro methodologies are typically favoured for their advantages in cost-effectiveness, user-friendliness, and tractable experimental design. For such approaches to be of value to pharmaceutical scientists, a tractable model that allowed for the rational addition or exclusion of specific ECM and formulation components is required. Such a model should include components that emulate critical elements of the IM injection site that might control drug binding and a means to monitor drug release from that environment over time under physiologically relevant conditions (McCartan et al., 2021).
Here, we have explored an initial design for a novel in vitro tool to model IM-specific drug fate, termed the Simulator of IntraMuscular Injections (abbreviated to SIMI). Col1 and HA were used in hydrogel formats to model potential interactions between the predominant protein and non-protein ECM materials that would be experienced by formulation components following their IM injection. These ECM-like hydrogels were contained within a semi-porous non-specific cuvette, which in turn was suspended in an ISF-mimic buffer solution to represent the infinite sink of the body following uptake into vascular or lymphatic capillaries. Initial tests using this format were performed using green fluorescent proteins (GFP) having three distinct global charge profiles alongside clinically relevant excipients (Lawrence et al., 2007;Pramanick et al., 2013). These GFP variants were selected as a surrogate drug to provide a means to readily visualise the injection bolus formed within the hydrogel and to simultaneously examine the impact of drug charge on drug-ECM binding events. Diffusion of these GFP variants from the ECM-hydrogels into the infinite sink compartment were monitored for rapid (first several hours) and extended release (release at 24 h) to examine the impact of biopharmaceutical properties and formulation components on drug fate. This prototype system showed that release of a material from a hydrogel mimicking the IM injection site was affected by ECM composition and that this release could be monitored by measuring drug entry into the surrounding ISFmimic solution. By following this diffusion over time, descriptive PK profiles of an injected drug and/or formulation materials could be obtained. These studies support the SIMI model as a potential in vitro model to screen IM-focused formulations for impact of ECM components on drug fate following injection.

Hydrogel generation
Bovine Col1 solution (Sigma C2124-50ML) and HA (Sigma 53,747-10G) were used in hydrogel preparations. 0.01 M HCl, prepared in double-distilled water ( dd H 2 O), was used to dilute Col1 solutions that were neutralized with 0.1 M NaOH prepared in dd H 2 O. 10× concentrated PBS stocks were prepared with NaCl (1.37 M), KCl (27 mM), Na 2 HPO 4 •7H 2 O (95 mM), KH 2 PO 4 (18 mM) and sodium azide (30 mM) in dd H 2 O, adjusted to pH 7.4 with HCl. Separate 10× PBS stocks were prepared including 5 mg/mL HA, mixed for at least 48 h to ensure complete solubilisation and homogenous mixing. 5 mL volume hydrogels were generated by diluting Col1 in 0.01 M HCl, and combining with 0.1 M NaOH and 10× concentrated PBS plus/minus 5 mg/mL HA in a ratio of 8:1:1, respectively, and incubated at 37 • C for at least 1 h. Final concentration of PBS in the hydrogel therefore equalled 1× PBS. For rheological assessment, 1.5 mL volume hydrogels were used containing 3.2 mg/mL Col1 and 0.5 mg/mL HA, or 3.2 mg/mL Col1 only. For the in vitro SIMI model, 5 mL volume hydrogels had final concentrations of 1 mg/mL Col1 and 0.5 mg/mL HA, or 1 mg/mL Col1 only.

Rheology
Rheological assessment of hydrogels was conducted with a Discovery HR-3 Hybrid Rheometer and Trios software (TA Instruments, DE, USA). A 40 mm Peltier steel plate was used in a parallel plate setup with a gap of 500 μm. 1.5 mL hydrogels were used for investigations. All experiments were conducted at plate temperatures of 37 • C. Each assessment was composed of three individual analyses. Oscillation frequency analysis was conducted at 0.5% strain, 0.1-100 rad/s. Oscillation amplitude analysis was conducted using an angular frequency of 6.28319 rad/s, with a strain range of 0.01-50%. Flow analysis was conducted over an increasing shear rate of 0.01 to 100 1/s with a 5 s equilibration time and 30s averaging time.

In vitro tool setup
140 mL of ISF buffer was decanted into a 150 mL beaker with a stirrer bar and warmed to 37 • C on a heated stirrer (200 rpm). The pH was then adjusted to 7.4. The 5 mL hydrogels were transferred into semi-permeable cuvettes of 6 mL volume (Pion #10900-0001, USA) and suspended with polystyrene floats in the warmed ISF buffer for at least 15 min prior to commencing experiments.

Expression of GFP, GFP + 36 and GFP-30
GFP and "supercharged" GFP proteins were prepared from plasmids containing the genes for GFP with a net − 7 charge (GFP-std), a net +36 charge (GFP-pos), and a net − 30 charge that were kindly provided by Dr. David Liu (Lawrence et al., 2007). Amino acid sequences for GFP-std, GFP-pos, and GFP-neg along with calculated values for molecular weight and isoelectric point (pI) are provided in Supplementary Data. All three GFP variants were expressed in Shuffle T7 Express competent E. coli (NEB) grown at 37 • C in Terrific broth supplemented with glycerol and containing kanamycin to an extinction of 0.6 at 600 nm. The temperature was reduced to 16 • C and isopropyl β-D-thiogalactopyranoside (IPTG) was added to a final concentration of 0.5 mM. Growth was continued at 16 • C overnight before cells were harvested by centrifugation, resuspended in 20 mM Tris pH 8, 1 M NaCl, 20 mM imidazole and lysed by sonication. Lysates were centrifuged and supernatant filtered through a 0.45-μm filter. Protein purification was performed using a 5-mL HisTrap™ column (GE Healthcare) connected to an AKTA FLPC. Bound proteins were eluted using a 0.05-1 M imidazole gradient and further purified using a Superdex® 200 gel filtration column equilibrated with PBS. Protein purity was assessed by SDS-PAGE and protein concentration was assessed using A280 prior to storage at − 80 • C.

GFP quantification
For GFP quantification, 100 μL ISF buffer duplicates were added to black plastic 96 well Nunclon Delta plates (Thermo #137101). The samples were measured for fluorescence intensity with a FLUOstar Omega plate reader using Omega control and data analysis software (BMG Labtech). The excitation wavelength was set at 480 nm, and the emission wavelength set at 520 nm. Data was transferred to Microsoft Excel for data analysis.

Organization of SIMI, an in vitro simulator of the intramuscular injection site
Plastic cuvettes (6 mL volume) with two opposing sides containing windows of dialysis membrane were filled with 5 mL hydrogels prepared from dominant ECM components present at the IM injection site: Col1 and Col1/HA. The hydrogel-filled cuvette was placed in a 140 mL stirred volume of media designed to emulate interstitial fluid (ISF), mimicking the infinite sink of the body, that could be used to measure release of molecules injected into the hydrogel over time (Fig. 1). By preparing hydrogels of different ECM elements, this system provided a tractable model to assess potential interactions between specific ECM elements and drug formulation components at an IM injection site prior to their absorption into blood or lymph. Thus, release characteristics of injected materials would be affected by specific and/or non-specific binding interactions with these ECM elements as well as biophysical properties of the hydrogel that affected diffusion. While the intent of the SIMI model was to emulate the human IM injection site, bovine Col1 was used in these initial studies rather than the human protein. BLASTp analysis using UniProt sequences for Col1 alpha-1 and alpha-2 chains (Supplementary Data Table 1) verified high sequence homology between bovine Col1 and human Col1, making this system realistic for modelling the human IM injection site. Amino acid homology of the bovine Col1 alpha-1 and alpha-2 chains was >90% identical to human Col1 alpha chains (Supplementary Data Table 2), indicating satisfactory similarity for use of bovine Col1 in the initial, proof-of-concept model.

Rheological characterisation reveals similar hydrogel viscoelastic properties
Rheological assessment initially studied the relationship between the loss modulus (G") and storage modulus (G') of hydrogel formats containing 3.2 mg/mL Col1 and 0.5 mg/mL HA, or 3.2 mg/mL Col1 alone across an increasing frequency range, known as a frequency sweep ( Fig. 2A). Sterile hydrogels of 1.5 mL volume used in these studies were consistently colourless and translucent. Tan(δ) values obtained using the Discovery HR-3 rheometer described the ratio between G" and G', i.e., dissipated energy and stored energy within the hydrogel matrix. Across the range of 0.1 to 100 rad/s frequency, the tan(δ) values decreased greatest between 0.1 and 10 rad/s for both Col1/HA and Col1 hydrogel formats. Past the 10 rad/s frequency point, the tan(δ) values remained stable. Results from both hydrogel formats overlapped across the entire frequency range studied, indicating similar elastic properties of both  (1) suspended a 5 mL hydrogel contained within a semi-porous cuvette (2), suspended in interstitial fluid-mimicking buffer (37 • C, pH 7.4) (3). The whole system was stirred (4) on a heated stirring plate (5). (B) Materials were directly injected (vertically) into the hydrogel at a 90 o angle to form a bolus. (C) Over time, injected material distributes through the hydrogel matrix, before releasing through the non-specific membranes into the buffer system. The buffer can then be sampled for quantifying drug at discreet time points.
Col1/HA and Col1 hydrogel formats. Amplitude sweeps were used to measure hydrogel viscoelasticity, where energy applied to the hydrogels was increased by enlarging the oscillation amplitude (Fig. 2B). Here, the Col1/HA format was less elastic than the Col1 alone format when considering G'. This could be because the extended chains of HA, extending throughout the Col1 network, reduced the amount of free space available for elastic deformation of Col1. The linear viscoelastic limit, however, was equal for both hydrogel formats at approximately 0.0016 rad (marked by a vertical dashed line; Fig. 2B). This indicated similar viscoelastic behaviour between the two hydrogel formats. Lastly, flow sweep assays characterized how these hydrogels "flowed" under increasing stress (Fig. 2C). The data indicated that both hydrogel formats possess the non-Newtonian characteristic of shear thinning: as the degree of stress applied to the hydrogels increased, viscosity decreased.
The Col1/HA format hydrogel exhibited lower viscosity than the Col1 alone format under shear rates of 0.1-10 1/s. At all other measured shear rate points, the data from both hydrogel formats again overlapped. Overall, these findings indicated how Col1/HA and Col1 hydrogels were similar in the rheological parameters studied and could be compared to each other in further experiments.

Protein charge affects injection site release characteristics
Green fluorescent protein (GFP) was selected as a surrogate to monitor distribution of an injected drug and to examine the impact of biomolecule charge on its release from the injection site compartment of the SIMI model. GFP, composed of 248 amino acids with a molecular mass of ~28 kDa, was prepared as three distinct genetic variants that had an overall charge of − 7 (GFP-std), supercharged negative at − 30 (GFP-neg), or supercharged positive at +36 (GFP-pos). These GFPs were prepared in non-buffered ISF-mimic solution (adjusted to pH 7.4) prior to injection into hydrogels prepared from 1 mg/mL Col1/0.5 mg/mL HA or 1 mg/mL Col1 alone. The extent of GFP-std and GFP-neg released from Col1/HA and Col1 hydrogel formats were comparable at 24 h, while the total amount of GFP-pos released over this time was much less compared to GFP-std or GFP-neg release (Fig. 3A). Data obtained over the first 5 h showed distinct release profiles for all three GFPs (Fig. 3B). The initial 10-min release profile of all GFP variants may be due to a portion of injected material that exceeded the extent of immediate hydrogel matrix interactions for the SIMI model format, with the subsequent release profile defining the release based upon hydrogel matrix interactions. Whilst currently undetermined, this may be a relevant in vivo event occurring post-IM injection. Release of GFP-pos reached a maximum after approximately 20-30 min for both Col1/HA and Col1 hydrogel formats. GFP-std release was comparable for both Col1/HA and Col1 hydrogel formats over the entire 24 h period, and similar to GFP-neg release from the Col1/HA hydrogel over 24 h (Fig. 3B). There was a general trend of increased release rates for GFP-pos and GFP-neg in the Col1 hydrogel format when compared to the Col1/HA format (Fig. 3B).

L-Histidine
L-Histidine is a physiological buffer commonly used to formulate proteins in the pH range of 5-7, utilizing the pKa 6.1 of its imidazole moiety (Osterberg and Wadsten, 1999). We examined the potential for this amino acid to affect the release of GFP variants from the hydrogels of pH 7.4 (maintained by phosphate of PBS), where L-histidine would be positively charged. Release of the three GFP variants formulated in 25 μM L-histidine in buffer-free ISF solution (the formulation pH was adjusted to pH 6) were studied over 24 h after their injection into the 1 mg/mL Col1/0.5 mg/mL HA or 1 mg/mL Col1 alone hydrogels (Fig. 4A). After 24 h, GFP-std release from Col1/HA and Col1 hydrogels as well as GFP-neg from the Col1/HA hydrogel were comparable, with the total amounts released being similar for these proteins formulated without Lhistidine (Fig. 3A). GFP-neg release from the Col1 only hydrogel was reduced in comparison to the other GFP test formats (Fig. 4A), and from that observed when GFP-neg was injected into Col1 alone hydrogels without L-histidine (Fig. 3A). The total amount of GFP-pos formulated in L-histidine released from Col1/ HA or Col1 hydrogel was further reduced compared to GFP-std and GFP-neg (Fig. 4A) but released to a greater degree than when this protein was formulated without L-histidine (Fig. 3A).
Differences in GFP release from these hydrogel formats was apparent after only 10 min when injected in L-histidine formulations (Fig. 4B). Initial GFP-pos release from either Col1/HA or Col1 hydrogels was reduced compared to profiles obtained without L-histidine (Fig. 3B), with greater retention of GFP-pos in these hydrogels over the first 5 h (Fig. 4B). GFP-neg formulated with L-histidine released steadily from the Col1 hydrogel but was initially retained like GFP-pos in both hydrogel formats (Fig. 4B). Further, GFP-neg released more readily from Col1/HA than Col1 hydrogels (Fig. 4B), where non-formulated GFP-neg released more readily from Col1 only hydrogels (Fig. 3B). In the case of GFP-std, release profiles over the first 5 h without added L-histidine were similar (Fig. 3B), but release from Col1/HA was faster than from Col1 alone when formulated with L-histidine (Fig. 4B).

Acetate
Acetate is a physiological buffer commonly used to formulate proteins in the pH range 2.5-7, utilizing the pKa 4.75 of acetic acid (Pramanick et al., 2013). We examined the potential for this weak acid to affect the release of GFP variants from the hydrogels of pH 7.4 (maintained by phosphate of PBS) where it would be negatively charged.
Release of GFPs formulated in 25 μM sodium acetate in buffer-free ISF (the formulation pH was adjusted to pH 6) were studied over 24 h after their injection into 1 mg/mL Col1/0.5 mg/mL HA or 1 mg/mL Col1 only (His) on the release of green fluorescent protein (GFP) in its standard form (GFP-std; circles), supercharged negative (GFP-neg; squares), or supercharged positive (GFP-pos; triangles) following injection into hydrogels composed of 1 mg/mL Col1/0.5 mg/ mL HA or 1 mg/mL Col1. (A) Release over 24 h. (B) Magnified release data over the first 5 h. Mean +/− standard deviation. GFP-std Col1, n = 4. GFP-std Col1/HA, n = 6. GFP-neg Col1, n = 4. GFP-neg Col1/HA, n = 6. GFP-pos all series, n = 4. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) hydrogels (Fig. 5A), with these outcomes being compared to release data obtained without added acetate (Fig. 3A). At 24 h, only one of the acetate-formulated systems showed limited release: GFP-pos injected into Col1/HA hydrogels. GFP-pos with acetate injected into Col1 only hydrogels released to a similar degree as GFP-neg from Col1 only gels after 24 h (Fig. 5A). All other formats demonstrated protein release outcomes at 24 h in the range expected based on non-formulated GFP data (Figs. 4A, 3A). Despite these similar ultimate release outcomes after 24 h, differences in release profiles were observed during the first 5 h after injection (Fig. 5B). Acetate-formulated GFP-std and GFP-neg showed fast, comparable release following injection into Col1/HA hydrogels. By comparison, these release profiles were diminished over the first 5 h after injection into Col1 alone hydrogels (Fig. 5B).

Polysorbate 20
Polysorbate 20 (PS20) is a non-ionic surfactant (formed by sorbitan ethoxylation prior to adding lauric acid) widely used in protein biopharmaceutical formulations as an excipient to stabilize emulsions and suspensions as well as minimize their binding to surfaces during purification and storage (Ionova and Wilson, 2020). Release of GFPs formulated in 30 mg/mL PS20 at pH 6 in non-buffered ISF-mimic solution were studied over 24 h after their injection into 1 mg/mL Col1/ 0.5 mg/mL HA or 1 mg/mL Col1 alone hydrogels (Fig. 6A) and compared to non-formulated GFP data (Fig. 3A). PS20 accelerated release of GFP-neg from Col1/HA hydrogels in the first 5 h (Fig. 6A). After 24 h, the extent of PS20-formulated GFP-neg release from Col1/HA hydrogels was the greatest degree of GFP-neg release in the studies performed (Figs. 3, 4, 5, 6). GFP-neg release from Col1, and GFP-std release from Col1/HA and Col1 hydrogels, was reduced compared to the moderate level of release observed in initial GFP studies (Figs. 6A, 3A). PS20 had little impact on the amount of GFP-pos released from Col1/HA or Col1 hydrogels, consistent with the release profiles of these highly cationic macromolecules in the formulation scenarios described above. PS20-formulated GFP-neg and GFP-std injected into Col1 hydrogels were not released as readily as that observed following injection into Col1/HA hydrogels (Fig. 6B).

Excipients affected GFP release based on protein charge and hydrogel composition
The physiological buffers L-histidine and acetate, and the non-ionic surfactant PS20, are commonly used in formulating injected protein therapeutics. These formulations, adjusted to pH 6, are not intended for long-term buffering of pharmaceuticals post-IM injection. This initial pH discrepancy would be swiftly resolved to physiological pH 7.4 in vivo, and is hypothesised to occur in the SIMI hydrogel (buffered to pH 7.4 with pospohate). This pH transition, as an event in itself, would likely be trivial in its impact on GFP release. These excipients were found to affect the release properties of three differently charged variants of GFP, dependent on the ECM components in the hydrogel. To compare the relative impact of these agents more readily, 24 h GFP release outcomes were plotted as the fraction of release over time (Fig. 7). The extent of impact produced by addition of these protein formulation components was expressed as the absolute difference to that observed for these three GFP variants lacking one of these additives.
With GFP-std, L-histidine or acetate demonstrated minimal impact on GFP-std release from the Col1/HA hydrogels (Fig. 7A). However, the surfactant PS20 did reduce GFP-std release from 81% to 67% at 24 h (Fig. 7A). This was a lesser impact than that observed in the Col1 hydrogel, reducing total GFP-std release from approximately 86% to 50% after 24 h (Fig. 7B). In the Col1 only hydrogel, inclusion of L-histidine or acetate reduced GFP-std release to 76% and 63%, respectively (Fig. 7B). Initial GFP-std release was consistent between all formulations in the Col1/HA format (Fig. 7A), whilst distinct release profiles were observed when using Col1 only hydrogels (Fig. 7B).
GFP-neg release over 24 h was 89% in Col1/HA hydrogels (Fig. 7C), comparable to 92% release observed in Col1 only hydrogels (Fig. 7D). PS20 caused a large initial increase in GFP-neg release within 5 h, that Fig. 6. Effect of 30 mg/mL polysorbate 20 (PS20) on the release of green fluorescent protein (GFP) in its standard form (GFP-std; circles), supercharged negative (GFP-neg; squares), or supercharged positive (GFP-pos; triangles) following injection into hydrogels composed of 1 mg/mL Col1/0.5 mg/ mL HA or 1 mg/mL Col1. (A) Release over 24 h. (B) Magnified release data over the first 5 h. Mean +/− standard deviation. All series n = 4. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) resulted in 100% GFP release at 24 h (Fig. 7C). This exceeded total release of GFP-neg when co-formulated with L-histidine (79%) or acetate (70%) in Col1/HA hydrogels (Fig. 7C). In the Col1 alone hydrogel, inclusion of L-histidine or acetate caused the greatest decrease in total GFP-neg release, 56% for both excipients, whilst PS20 had a lesser impact, reducing total GFP-neg release to 77% (Fig. 7D).

Discussion
The in vitro tool we describe here, referred to as the SIMI, was designed to test an initial in vitro system concept to model IM injected material distribution and release in vivo. IM injections result in the distribution of drug formulations within the endomysium and perimysium of striated muscle. While a variety of cell types are found in these locations (fibroblasts, endothelial cells, etc.), the SIMI does not Fig. 7. Total release of (A, B) native GFP, (C, D) supercharged negative GFP and (E, F) supercharged positive GFP was compared to formulations containing 25 μM L-histidine (His; squares), 25 μM sodium acetate (Ace; diamonds) or 30 mg/mL polysorbate 20 (PS20; triangles) over 24 h. Hydrogels were composed of (A, C & E) 1 mg/mL Col1/0.5 mg/mL HA or (B, D & F) 1 mg/mL Col1. (E & F) Inset magnifies initial release of GFP-pos. Mean +/− standard deviation. Data is reformulated from Figs. 3, 4, 5, 6 with corresponding n numbers.
incorporate cells and thus is not amenable to testing formations such as RNA-based vaccines, that involve cell-based outcomes. Instead, the SIMI is intended to examine the fate of IM-injected therapeutics where the fate of these materials would be potentially affected by interactions with acellular inter-fascial ECM components that dominate these regions. This ECM is primarily composed of Col1, imparting strength and a framework for interactions with other ECM elements. HA is one of most prominent of these other elements, functioning as a biological lubricant to retain water and minimize friction between fascial layers during dynamic muscle movement (McCartan et al., 2021). Based upon their relative abundance, these ECM components were identified for this initial modelling of the IM injection site. For these studies, we established a Col1 hydrogel format at physiological pH (7.4) and temperature (37 • C) that was by itself, and in combination with HA, translucent and colourless. These features allow for direct visual and spectroscopic assessment of events within the compartment following introduction of a test formulation. Our proof-of-concept format used 5 mL volumes of these two hydrogel matrices as injection compartment that could accept 200 μL volumes of a test formulation in an arrangement that allowed detection of released materials into an ISF-mimic buffer that modelled the infinite sink of the body.
The hydrogel matrix and buffer designs used here were simplistic; both the IM fascial layers and ISF contain many components not represented in the current SIMI design, such as soluble proteins in the ISF (which may affect the release of poorly water-soluble drugs in vivo). Buffering elements used in the current SIMI format were also nonphysiological: phosphate (presented in PBS) in the hydrogel, and HEPES in the ISF-mimic solution. These buffers were selected for their properties and ease of use in generating a pilot tool. PBS has been frequently applied in Col1-based hydrogel design and shown to be chemically stable in a pH 7.4 solution. HEPES is also chemically stable in aqueous settings, with a pKa of 7.5 that was ideal to maintain pH 7.4 in the ISF-mimic buffer. Both PBS and HEPES offered simplified alternatives to a more complex, bio-relevant buffer which can be difficult to maintain at a desired pH over extended periods (Kinnunen et al., 2015). Further research should replace PBS and HEPES with bicarbonate buffering components to reflect this foundational element of the ISF in vivo. This will, however, require technologies to continuously monitor pH and administer CO 2 gas accordingly to maintain pH 7.4 in the SIMI environments. However, there is no guarantee that specific IM environment components, such as bicarbonate, will impact drug fate outcomes. Candidate components for inclusion in the SIMI compartments must therefore be evaluated accordingly.
The two hydrogel formats we describe here were assessed for their rheological properties. Both hydrogel formats were indeed similar in viscoelasticity and non-Newtonian behaviour; shear thinning is welldocumented in biomaterial hydrogels and has been exploited in injectable-hydrogel drug-delivery strategies Loebel et al., 2017). However, the 3.2 mg/mL Col1 content used in rheological analysis was too high for injection modelling in the SIMI, as injected materials were immediately ejected along the needle bore. This frustrated efforts to form an injection bolus, which was desired in the SIMI to reflect in vivo observations of injected pharmaceuticals forming bolus deposits (Darville et al., 2016). Reducing Col1 content to 1 mg/mL produced stable hydrogels that were sufficiently malleable for bolus formation and dissemination through the hydrogel matrix following injection of a formulation. By sampling the ISF-mimic buffer over 24 h, we established the release profiles of charged GFP variants, in several formulations, from boluses formed in the hydrogel environment. The hydrogel matrix of Col1 and HA may influence movement of molecules by steric hindrance; bulkier drugs, such as macromolecules, may be retained longer at the injection site while small molecules could more freely move through the Col1/HA network. Of these components, Col1 is noted to be positively charged at physiological pH (Andrade et al., 2004;Li and Katz, 1976), whilst HA is negatively charged (Travis et al., 2001). These distinctly charged components may influence diffusion of strongly charged molecules and macromolecules through electrostatic interactions, further retaining molecules in the hydrogel matrix after injection or encouraging their diffusion from the matrix and into the infinite sink compartment.
In the SIMI, we observed that GFP-std and GFP-neg generally displayed negative exponential curve release profiles. These indicate a decay of GFP presence at the injection site as GFP diffuses through the hydrogel matrix (with no distinct inhibitory interactions) before releasing into the ISF-mimic buffer. These non-formulated GFPs were prepared for injection with no buffering agents; there are no other potential interactions occurring other than GFP with the hydrogel and ISFmimic systems. However, GFP-pos generally released slowly from the injection site, independent of hydrogel composition, suggesting specific interactions with the Col1 matrix. In this regard, several proteins, including von Willebrand factor and various growth factors, specifically bind Col1 in vivo (Cauble et al., 2017;Liu et al., 2003;Romijn et al., 2003).
Clear differences were observed in the release profiles of GFP over the 24-h window of analysis that were assumed to describe interaction parameters between the distinct physical properties of the GFP variants and Col1 or Col1/HA matrices. Interestingly, we observed a similar initial release of all non-formulated GFP variants at 10 min postinjection that did not reflect the extent of release after 24 h. The reason for this phenomenon is unclear but could be due to pressure introduced into the hydrogel by the injection process. This added force may have been sufficient to transiently overcome the interactive forces between the injected GFP variant and the hydrogel components that ultimately defined the extent of release observed after 24 h. As materials then "settle" post-injection, the injection-pressure force should diminish. Interactions with the hydrogel matrix would dominate and reflect interactions that control diffusion away from the bolus site. Interestingly, the extent of this initial "burst" observed at 10 min postinjection was dramatically altered for studies that examined additions of L-histidine, acetate, or PS20. Here, the release at 10 min post-injection directly correlated with the extent of release after 24 h, suggesting that the presence of these additives affected the extent of initial interactions during this "burst" between the GFP variant and the Col1 or Col1/HA hydrogels in a way that overcame the added pressure associated with the injection event. This impact of excipient presence on this initial "burst" and 24 h release was further investigated by exploiting the tractable nature of the SIMI.
By using Col1/HA and Col1 only hydrogel formats, we examined the individual roles Col1 and HA in determining drug fate outcomes for these GFP variants. Absence of HA in hydrogels did not affect nonformulated GFP-std release, but reduced release when formulated with L-histidine, acetate or PS20 (Figs. 7A 7B). Yet, non-formulated GFP-pos release over 24 h was doubled in the presence of HA (Fig. 7E, F). Such data indicated how HA can affect IM drug fate outcomes, dependent on drug characteristics and distinct from the effects of Col1. Excipients, too, appeared to alter this interaction dynamic between GFP and the hydrogel matrix, dependent on GFP charge and/or hydrogel composition. For example, L-histidine increased GFP-pos release from both hydrogel formats, while acetate or PS20 reduced release. Indeed, acetate generally reduced diffusion of all GFP variants in both hydrogel formats. Whilst the mechanisms of these observations are unclear, a histidinerich glycoprotein can block Col1 interactions with endothelial cells (Roche et al., 2015) while sodium eugenol acetate (derived from sodium acetate) inhibited Col1-dependent platelet aggregation in vitro (Chen et al., 1996). Hypothetically, L-histidine and acetate may directly interact with Col1 networks, reducing interactions with GFP. Distinct in function from L-histidine and acetate, surfactant PS20's ability to stabilize proteins at interfaces may have led to altered protein-hydrogel matrix interactions. Whilst PS20 generally reduced the release of all GFP variants in all hydrogels, such stabilisation may be significant for strongly negative macromolecules: GFP-neg formulated with PS20 in Col1/HA hydrogels exhibited expedited diffusion away from the injection site (Fig. 7C). Yet, PS20 decreased release of GFP-neg compared to non-formulated GFP-neg in Col1 only hydrogels (Fig. 7D), indicating HA-surfactant interplay. Presently, the mechanisms relating to such outcomes with PS20 (and indeed L-histidine and acetate) are not clear. However, the SIMI was designed to explore such examples of drug fate outcomes in a fully tractable manner, to establish how excipients modify drug behaviour post-IM administrations. As such, with further development and application of the SIMI, such mechanisms could be elucidated.
In formulation design, the long-term stability of a drug is essential to allow storage of the product before administration. As such, formulations may use low concentrations (i.e., 25 μM) of buffering excipients, as used in this study, for maintaining non-physiological pH (i.e., pH 6). However, such designs are not intended for buffering drug formulations in vivo post-administration. These non-physiological pH conditions will likely rapidly transition to physiological pH post-injection, and are modelled here in the SIMI (i.e., μM quantities of formulation excipient, versus overwhelming mM quantities of phosphate in the hydrogel). While the initial change in pH of the environment is likely to be inconsequential, the results of this change may have a role in drug fate. Transitions to physiological pH may alter the overall charge state of excipients with respect to their pKa values, thereby possibly influencing potential interactions with the local IM environment.
While this initial version of the SIMI model has begun to uncover the role of drug, excipient, and injection site properties and their interplay in drug fate outcomes, it must be acknowledged that further work is crucial in establishing its true value in modelling human IM conditions. Evaluation and validation of the SIMI by in vitroin vivo correlations (IVIVC), and indeed mathematical modelling of drug PK, have yet to be undertaken. While IVIVC data could be explored to establish e.g., equivalence of the SIMI to standard in vivo models such as rats, it is the ability to model human PK outcomes that is paramount for SIMI model applications. Such validation can indeed be conducted using published human PK data from the PharmaPendium database (Elsevier), which contains data for a wide range of IM-approved pharmaceuticals, from small molecules to proteins, in an array of quick or slow releasing formulations. Examples of drugs with such PK data available have been tabulated (McCartan et al., 2021).
The GFP variants used in these studies provided a surrogate set of molecules to emulate protein biopharmaceuticals with distinct charge properties. In this initial SIMI model format, the potential for charge interactions between biopharmaceuticals and ECM components were examined and found to dominate release characteristics from the hydrogel matrices composed of Col1 and HA. Furthermore, our with these GFP variants demonstrated how formulation excipient choices might impact IM drug fate outcomes. Further development of SIMI technologies would permit further exploration of potential events affecting drug fate in vivo, such as whether individual excipients directly interacting with IM site components, such as Col1. This kind of descriptive data, informing behavioural trends of injected materials, may be useful to inform rational drug formulation design for IM administration.

Conclusions
IM drug fate is an area of limited knowledge, despite the increasing application of IM pharmaceuticals in the clinic. Here, we have described an initial effort to establish a tractable in vitro tool for investigating parameters of drug fate after IM administrations. From the data presented and discussed here, the SIMI appears to allow discrimination of distinct biopharmaceutical properties and can evaluate specific formulation excipients that may impact drug performance at the IM site in a tractable manner. Using the model, we have begun to identify pharmaceutical and formulation properties that can affect drug fate and to determine how specific ECM components of the IM injection site can likewise impact drug fate. Such an approach could be useful in formulation screening studies to predict the likely performance of a pharmaceutical formulation to expedite research efforts by reducing dependence on costly, time-consuming in vivo experiments that may not actually be productive of outcomes in humans. Thus, after obtaining these initial proof-of-concept outcomes, efforts to use human ECM components and use a more physiologically relevant ISF composition are being contemplated for a next generation SIMI model.

Research data
All data supporting this study is openly available from the University of Bath Research Data Archive (https://doi.org/10.15125/B ATH-01146).